基于Google云平臺的TensorFlow高級機器學習專項課程

Advanced Machine Learning with TensorFlow on Google Cloud Platform

Learn Advanced Machine Learning with Google Cloud. Build production-ready machine learning models with TensorFlow on Google Cloud Platform.

Google Cloud

Coursera

計算機

難(高級)

2 個月

本課程由Coursera和Linkshare共同提供
  • 英語
  • 1002

課程概況

This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.

包含課程

課程1
End-to-End Machine Learning with TensorFlow on GCP

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp). One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform Prerequisites: Basic SQL, familiarity with Python and TensorFlow >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service

課程2
Production Machine Learning Systems

In the second course of this specialization, we will dive into the components and best practices of a high-performing ML
system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

課程3
Image Understanding with TensorFlow on GCP

This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

課程4
Sequence Models for Time Series and Natural Language Processing

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. ? Predict future values of a time-series ? Classify free form text ? Address time-series and text problems with recurrent neural networks ? Choose between RNNs/LSTMs and simpler models ? Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

程5
Recommendation Systems with TensorFlow on GCP

In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. ? Devise a content-based recommendation engine ? Implement a collaborative filtering recommendation engine ? Build a hybrid recommendation engine with user and content embeddings >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service

預備知識

To get the most out of this specialization, participants should have:

Knowledge of machine learning and TensorFlow to the level of the first Machine Learning on GCP specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp)

Experience coding in Python

Knowledge of basic statistics

Knowledge of SQL and cloud computing (helpful)

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